The problem addressed in the paper is how to represent the knowledge associated with design decision models to enable storage, retrieval, and reuse. The paper concerns the representations and reasoning mechanisms needed to construct decision models of relevance to engineered product development. Specifically, AL[E][N] description logic is proposed as a formalism for modeling engineering knowledge and for enabling retrieval and reuse of archived models. Classification hierarchies are constructed using subsumption in DL. Retrieval of archived models is supported using subsumption and query concepts. In our methodology, design decision models are constructed using the base vocabulary and reuse is supported through reasoning and retrieval capabilities. Application of the knowledge representation for the design of a cantilever beam is demonstrated.
This paper details a method of change prediction that builds upon the traditional component-component design structure matrix by incorporating manufacturing costs and modeling higher orders of coupling. A coupling index is also created to assess the level of coupling between interfaced features. A BMW X5 headliner assembly and a Ryobi hand-held drill are analyzed using the proposed method to identify the features and components that offer the greatest ease of change. The analysis of the BMW X5 headliner shows that the rectangular slots on the bottom of the adapter plates are the feature that offers the greatest ease of change, while the handles are the component that offers the greatest ease of change. For the Ryobi drill, the battery is identified as the component that offers the greatest ease of change. The proposed method of change prediction proves to be an effective and efficient means of modeling change propagation and assessing change impact.
Abstract Simulation models are often used in design to predict system performance for several use cases including trade space exploration, decision-making, and validation and verification. Models are abstractions of reality and do not contain all the phenomena and details in the real world. This fact brings many concerns, such as “How can I trust this model?” or “How should I choose between models?”. In modeling and simulation (M&S), the concept of fidelity explains how a model differs from reality. This paper proposes a set-based definition of fidelity based on the reduction of information throughout the model development process. An example of a ground vehicle conducting a gradeability test demonstrates the reduction in information from reality, the known world, testing, modeling, and simulation. Overall, this set-based approach to fidelity bridges disparate definitions of fidelity and creates a greater understanding of how models reflect reality.
Haptic perception of fine surface features is a fundamental modality to identify virtual objects.Roughness and stickiness, which are modeled as surface textures and friction respectively, are the main characteristics in terms of haptics.This research is aimed at the haptic rendering method of fine surface features based on the analysis of the surface profile.Functionally generated surface features are employed for the haptic rendering of surface textures and surface friction.Haptic rendering of anisotropic surface -surface having a dominant feature direction, and haptic rendering of heterogeneous surface -surface with a varied feature density, are investigated.Experimental measurements and prototype system implementations have been done to show the fidelity of the proposed surface feature modeling methods.
Abstract develop a survey consisting of statements that provide insight into an individual's design space exploration tendency. There are formal exercises to evaluate design space exploration, but these exercises are resource intensive, time consuming, challenging to deploy and difficult to process the results. The survey instrument is intended to address several of these challenges. To develop the survey instrument, the Shah-Vargas (SV) metrics of engineering ideation effectiveness were used as a basis for quantifying engineering Design Space Exploration (DSE). These metrics are 1) Quantity – the number of ideas generated, 2) Quality – the conformance of each idea to engineering requirements, 3) Variety – the dissimilarity of an idea within an individual's set of generated ideas, and 4) Novelty – the dissimilarity of an idea within the collectively exhaustive set of ideas. With these metrics as a guide, an initial list of statements was developed using two approaches. First, literature was reviewed for statements that have been used to collect self-reported data on the four metrics. Second, the definitions of the four metrics from Shah and colleagues were reviewed and converted to question form. This resulted in four statements per metric, totaling 16 statements. Next, to assess question clarity regarding the four metrics and to ensure survey respondents accurately grasped the metric each statement pertained to, Latent Semantic Analysis (LSA) was employed to evaluate overlap. In addition, the statements were processed by a Large Language Model which was asked to assess overlap. Based on the findings from these analyses, the statements were modified to reduce overlap. A final verification of mutual exclusivity will be where participants are going to be asked to categorize each question into one of the four metrics. The result of this work is a survey with statements which allows an individual to self-report their DSE tendency. In the future, this validity of self-reported data will be assessed by comparing it with direct assessment of DSE tendency. Once validated, the DSE survey is intended for researchers to gain a deeper understanding about DSE tendencies without having the resource-intensive, subjective task of performing direct assessments. Additionally, the survey can be used as a pre-screening if/when design exercises are deployed.
This paper explores the possibility of supporting automated function-based reasoning in the conceptual design phase, specifically, reasoning needed to perform physics-based concept validation. Eleven atomic tasks of topologic reasoning, divided in two categories, connectedness and derivation, are identified that could be used to check graph-based function structures against conservation laws using only the count and types of flows attached to the functions. This reasoning is illustrated by simulating the sequential actions of a designer developing a new mechanical device. Next, recently proposed formal definitions of function verbs are used to explore the possibility of supporting additional quantitative reasoning toward conservational concept validation. Finally, these findings are used to identify information elements that must be captured in a formal representation of mechanical functions in order to support this reasoning.
Multidisciplinary design optimization (MDO) is a methodology, which integrates analysis and optimization techniques to solve engineering design problems involving multiple disciplines. MDO frameworks have been developed which facilitate the integration of disciplinary analysis code and optimization techniques. Commonly used MDO frameworks include ModelCenter, iSIGHT, modeFRONTIER and FIDO. Recent advances in MDO framework have addressed issues related to data exchange, distributed computing, process integration and trade study. However managing, storing and sharing MDO problem information have not yet been addressed. Thus, the primary objective in this research is to propose a new configuration for MDO frameworks to enhance information management capabilities. This is achieved by (1) identifying requirements for managing decision-related information, (2) evaluating current MDO frameworks against the key information management requirements, and (3) extending current software to include a file information system and structured repository. Finally, a new configuration is developed augmenting a structured repository to enable reuse of information associated with the formulation of MDO problems.
Functional representations are often used in the conceptual stages of design because they encourage the designer to focus on the intended use and purpose of a system rather than the physical solution. Function models have been proposed by many researchers as a tool to expand the solution search space and guide concept generation, and many design tools have been created to support function-based design. These tools require designers to create function models of new or existing artifacts, but there is limited published research describing the level of functional detail that should be included in a model or the appropriate level of abstraction to model artifacts. Further, there is limited experimental evidence that designers use function models when generating concepts, and controlled experiments in the literature have focused on ideation rather than function models. Therefore, this research focuses on how artifacts should be modeled to guide concept generation in conceptual design. In this research, three artifact representations are studied: function models, interaction models, and pruned function models. A user study was conducted in which participants were asked to design a new device based on a problem statement, a set of requirements, and a treatment. Participants were randomly assigned a treatment of a function model, interaction model, pruned model, or no model. A conformance metric was developed to measure the extent to which participants used a model when generating concept sketches. The results show that the functional conformance of participants using a pruned model is approximately 40% higher than that of participants using a function model. These results demonstrate that the use of a specific level of functional detail improves the use of functions within the model for concept generation.
